Manifold learning in data mining tasks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)


Many Data Mining tasks deal with data which are presented in high dimensional spaces, and the 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving these tasks. To avoid these phenomena, various Representation learning algorithms are used as a first key step in solutions of these tasks to transform the original high-dimensional data into their lower-dimensional representations so that as much information about the original data required for the considered Data Mining task is preserved as possible. The above Representation learning problems are formulated as various Dimensionality Reduction problems (Sample Embedding, Data Manifold embedding, Manifold Learning and newly proposed Tangent Bundle Manifold Learning) which are motivated by various Data Mining tasks. A new geometrically motivated algorithm that solves the Tangent Bundle Manifold Learning and gives new solutions for all the considered Dimensionality Reduction problems is presented.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 10th International Conference, MLDM 2014, Proceedings
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319089782
Publication statusPublished - 2014
Externally publishedYes
Event10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014 - St. Petersburg, Russian Federation
Duration: 21 Jul 201424 Jul 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8556 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014
Country/TerritoryRussian Federation
CitySt. Petersburg


  • Data Mining
  • Dimensionality Reduction
  • Manifold Learning
  • Representation learning
  • Statistical Learning
  • Tangent Bundle Manifold Learning
  • Tangent Learning


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